Convolutional Neural Networks

Project: Write an Algorithm for a Dog Identification App


In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!

Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the Jupyter Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.

In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.

Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.

The rubric contains optional "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this Jupyter notebook.


Why We're Here

In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).

Sample Dog Output

In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. Your imperfect solution will nonetheless create a fun user experience!

The Road Ahead

We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.

  • Step 0: Import Datasets
  • Step 1: Detect Humans
  • Step 2: Detect Dogs
  • Step 3: Create a CNN to Classify Dog Breeds (from Scratch)
  • Step 4: Create a CNN to Classify Dog Breeds (using Transfer Learning)
  • Step 5: Write your Algorithm
  • Step 6: Test Your Algorithm

Step 0: Import Datasets

Make sure that you've downloaded the required human and dog datasets:

Note: if you are using the Udacity workspace, you DO NOT need to re-download these - they can be found in the /data folder as noted in the cell below.

  • Download the dog dataset. Unzip the folder and place it in this project's home directory, at the location /dog_images.

  • Download the human dataset. Unzip the folder and place it in the home directory, at location /lfw.

Note: If you are using a Windows machine, you are encouraged to use 7zip to extract the folder.

In the code cell below, we save the file paths for both the human (LFW) dataset and dog dataset in the numpy arrays human_files and dog_files.

In [2]:
import torch
torch.cuda.get_device_name(0)
!nvidia-smi
Tue Sep 17 08:22:17 2019       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 430.40       Driver Version: 430.40       CUDA Version: 10.1     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  TITAN Xp COLLEC...  Off  | 00000000:26:00.0  On |                  N/A |
| 24%   42C    P5    28W / 250W |    646MiB / 12181MiB |      8%      Default |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0      1416      G   /usr/lib/xorg/Xorg                            36MiB |
|    0      1449      G   /usr/bin/gnome-shell                          52MiB |
|    0      1808      G   /usr/lib/xorg/Xorg                           363MiB |
|    0      1940      G   /usr/bin/gnome-shell                         151MiB |
|    0      2595      G   /usr/lib/firefox/firefox                       3MiB |
|    0      2654      G   /usr/lib/firefox/firefox                       3MiB |
+-----------------------------------------------------------------------------+
In [3]:
import numpy as np
from glob import glob

# load filenames for human and dog images
human_files = np.array(glob("data/lfw/*/*/*"))
dog_files = np.array(glob("data/dog_images/*/*/*"))

# print number of images in each dataset
print('There are %d total human images.' % len(human_files))
print('There are %d total dog images.' % len(dog_files))
There are 18982 total human images.
There are 8351 total dog images.

Step 1: Detect Humans

In this section, we use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images.

OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the haarcascades directory. In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.

In [4]:
!pip install opencv-python
import cv2                
import matplotlib.pyplot as plt                        
%matplotlib inline                               

# extract pre-trained face detector
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')

# load color (BGR) image
img = cv2.imread(human_files[100])


# for i in range(100):    
#     print(human_files[i])
    
# convert BGR image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# find faces in image
faces = face_cascade.detectMultiScale(gray)

# print number of faces detected in the image
print('Number of faces detected:', len(faces))

# get bounding box for each detected face
for (x,y,w,h) in faces:
    # add bounding box to color image
    cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
    
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# display the image, along with bounding box
plt.imshow(cv_rgb)
plt.show()
Requirement already satisfied: opencv-python in /home/evan/anaconda3/lib/python3.7/site-packages (4.1.1.26)
Requirement already satisfied: numpy>=1.14.5 in /home/evan/anaconda3/lib/python3.7/site-packages (from opencv-python) (1.16.2)
Number of faces detected: 1

Before using any of the face detectors, it is standard procedure to convert the images to grayscale. The detectMultiScale function executes the classifier stored in face_cascade and takes the grayscale image as a parameter.

In the above code, faces is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x and y) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w and h) specify the width and height of the box.

Write a Human Face Detector

We can use this procedure to write a function that returns True if a human face is detected in an image and False otherwise. This function, aptly named face_detector, takes a string-valued file path to an image as input and appears in the code block below.

In [5]:
# returns "True" if face is detected in image stored at img_path
def face_detector(img_path):
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray)
    return len(faces) > 0

(IMPLEMENTATION) Assess the Human Face Detector

Question 1: Use the code cell below to test the performance of the face_detector function.

  • What percentage of the first 100 images in human_files have a detected human face?
  • What percentage of the first 100 images in dog_files have a detected human face?

Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face. You will see that our algorithm falls short of this goal, but still gives acceptable performance. We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays human_files_short and dog_files_short.

Answer: 98% Accuracy in human images. 83% Accuracy in dog images.

In [6]:
from tqdm import tqdm

human_files_short = human_files[:100]
dog_files_short = dog_files[:100]

#-#-# Do NOT modify the code above this line. #-#-#

## TODO: Test the performance of the face_detector algorithm 
## on the images in human_files_short and dog_files_short.
humancounter = 0
dogcounter = 0
for face in human_files_short:
    if face_detector(face): humancounter +=1
for dog in dog_files_short:
    if face_detector(dog): dogcounter +=1
        
print("faces detected in humans: " + str(humancounter))
print("faces detected in dogs: " + str(dogcounter))
faces detected in humans: 97
faces detected in dogs: 10

We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :). Please use the code cell below to design and test your own face detection algorithm. If you decide to pursue this optional task, report performance on human_files_short and dog_files_short.

In [7]:
### (Optional) 
### TODO: Test performance of anotherface detection algorithm.
### Feel free to use as many code cells as needed.

Step 2: Detect Dogs

In this section, we use a pre-trained model to detect dogs in images.

Obtain Pre-trained VGG-16 Model

The code cell below downloads the VGG-16 model, along with weights that have been trained on ImageNet, a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of 1000 categories.

In [8]:
import torch
import torchvision.models as models

# define VGG16 model
VGG16 = models.vgg16(pretrained=True)

# check if CUDA is available
use_cuda = torch.cuda.is_available()

# move model to GPU if CUDA is available
if use_cuda:
    print("using cuda")
    VGG16 = VGG16.cuda()
using cuda

Given an image, this pre-trained VGG-16 model returns a prediction (derived from the 1000 possible categories in ImageNet) for the object that is contained in the image.

(IMPLEMENTATION) Making Predictions with a Pre-trained Model

In the next code cell, you will write a function that accepts a path to an image (such as 'dogImages/train/001.Affenpinscher/Affenpinscher_00001.jpg') as input and returns the index corresponding to the ImageNet class that is predicted by the pre-trained VGG-16 model. The output should always be an integer between 0 and 999, inclusive.

Before writing the function, make sure that you take the time to learn how to appropriately pre-process tensors for pre-trained models in the PyTorch documentation.

In [9]:
from PIL import Image
import torchvision.transforms as transforms

def VGG16_predict(img_path):
    '''
    Use pre-trained VGG-16 model to obtain index corresponding to 
    predicted ImageNet class for image at specified path
    
    Args:
        img_path: path to an image
        
    Returns:
        Index corresponding to VGG-16 model's prediction
    '''
    
    ## TODO: Complete the function.
    ## Load and pre-process an image from the given img_path
    ## Return the *index* of the predicted class for that image
    

    transform = transforms.Compose([           
        transforms.Resize(256),                    
        transforms.CenterCrop(224),  # or  transforms.RandomSizedCrop(224)  ??
        transforms.ToTensor(),                     
        transforms.Normalize(                      
        mean=[0.485, 0.456, 0.406],                
        std=[0.229, 0.224, 0.225]
        )])
    

    img = Image.open(img_path)    
    imgt = transform(img)
    
    batcht = torch.unsqueeze(imgt, 0)
    if use_cuda:
        batcht = batcht.cuda()
        
    VGG16.eval()
    results = VGG16(batcht) 
#    print(results.shape)
    
    
    return results # predicted class index

(IMPLEMENTATION) Write a Dog Detector

While looking at the dictionary, you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from 'Chihuahua' to 'Mexican hairless'. Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained VGG-16 model, we need only check if the pre-trained model predicts an index between 151 and 268 (inclusive).

Use these ideas to complete the dog_detector function below, which returns True if a dog is detected in an image (and False if not).

In [10]:
### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
    ## TODO: Complete the function.
    out = VGG16_predict(img_path)

    _, index = torch.max(out, 1)

    return 150 < index <= 268  # true/false

(IMPLEMENTATION) Assess the Dog Detector

Question 2: Use the code cell below to test the performance of your dog_detector function.

  • What percentage of the images in human_files_short have a detected dog?
  • What percentage of the images in dog_files_short have a detected dog?

Answer:

dogs detected in humans: 1%

dogs detected in dogs: 100%

In [71]:
### TODO: Test the performance of the dog_detector function
### on the images in human_files_short and dog_files_short.

humancounter = 0
dogcounter = 0
for face in human_files_short:
    if dog_detector(face): humancounter +=1
for dog in dog_files_short:
    if dog_detector(dog): dogcounter +=1

print("dogs detected in humans: " + str(humancounter) + " % ")
print("dogs detected in dogs: " + str(dogcounter) + " %")        
        
dogs detected in humans: 1 % 
dogs detected in dogs: 100 %

We suggest VGG-16 as a potential network to detect dog images in your algorithm, but you are free to explore other pre-trained networks (such as Inception-v3, ResNet-50, etc). Please use the code cell below to test other pre-trained PyTorch models. If you decide to pursue this optional task, report performance on human_files_short and dog_files_short.

In [12]:
### (Optional) 
### TODO: Report the performance of another pre-trained network.
### Feel free to use as many code cells as needed.

Step 3: Create a CNN to Classify Dog Breeds (from Scratch)

Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, you will create a CNN that classifies dog breeds. You must create your CNN from scratch (so, you can't use transfer learning yet!), and you must attain a test accuracy of at least 10%. In Step 4 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.

We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that even a human would have trouble distinguishing between a Brittany and a Welsh Springer Spaniel.

Brittany Welsh Springer Spaniel

It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).

Curly-Coated Retriever American Water Spaniel

Likewise, recall that labradors come in yellow, chocolate, and black. Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.

Yellow Labrador Chocolate Labrador Black Labrador

We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.

Remember that the practice is far ahead of the theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun!

(IMPLEMENTATION) Specify Data Loaders for the Dog Dataset

Use the code cell below to write three separate data loaders for the training, validation, and test datasets of dog images (located at dog_images/train, dog_images/valid, and dog_images/test, respectively). You may find this documentation on custom datasets to be a useful resource. If you are interested in augmenting your training and/or validation data, check out the wide variety of transforms!

In [13]:
import os
from torchvision import datasets,transforms
import torch


#try to fix the image file truncated error ?
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True


### TODO: Write data loaders for training, validation, and test sets
## Specify appropriate transforms, and batch_sizes

#normalization = transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
normalization =  transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])

test_trans = transforms.Compose([transforms.Resize(255),
                                 transforms.CenterCrop(224),
                                 transforms.ToTensor(),
                                 normalization])

train_trans = transforms.Compose([transforms.Resize(255),
                                    transforms.RandomRotation(20),
                                       transforms.RandomResizedCrop(224),
                                       transforms.RandomHorizontalFlip(),
                                       transforms.ToTensor(),
                                       normalization])

valid_trans = transforms.Compose([transforms.Resize(255),
                                 transforms.CenterCrop(224),
                                 transforms.ToTensor(),
                                 normalization])


trainset = datasets.ImageFolder('data/dog_images/train',transform=train_trans)
trainloader = torch.utils.data.DataLoader(trainset,batch_size=256,shuffle=True)

testset = datasets.ImageFolder('data/dog_images/test',transform=test_trans)
testloader =  torch.utils.data.DataLoader(testset,batch_size=256)

validset = datasets.ImageFolder('data/dog_images/valid',transform=valid_trans)
validloader = torch.utils.data.DataLoader(validset,batch_size=256)

loaders_scratch = {
    'train' : trainloader, #DataLoader(image_datasets['train'],batch_size = batch_size,shuffle=True),
    'valid' : validloader,#DataLoader(image_datasets['valid'],batch_size = batch_size),
    'test' : testloader #DataLoader(image_datasets['test'],batch_size = batch_size)       
}


# images,labels = next(iter(trainloader))
# images[0]

Question 3: Describe your chosen procedure for preprocessing the data.

  • How does your code resize the images (by cropping, stretching, etc)? What size did you pick for the input tensor, and why?
  • Did you decide to augment the dataset? If so, how (through translations, flips, rotations, etc)? If not, why not?

Answer:

  • The code resizes images by first stretching a bit bigger, then cropping out the center back to the original 224 pixel size. Since in most pictures the subject is in the center of the picture this should cut out unneeded data. 224 is a good size that divides by 2 nicely for each pooling layer

  • Yes, data augmentation is a good approach for minimizing overfitting. For this I did randomly flipping, and random slight rotation no more than 20 degrees.

(IMPLEMENTATION) Model Architecture

Create a CNN to classify dog breed. Use the template in the code cell below.

In [14]:
import torch.nn as nn
import torch.nn.functional as F

# define the CNN architecture
class Net(nn.Module):
    ### TODO: choose an architecture, and complete the class
    def __init__(self):
        super(Net, self).__init__()
        ## Define layers of a CNN
        
        #224x224 in
        
        #conv layers
        
        self.conv1 = nn.Conv2d(3, 16, 3, stride=1, padding=1)

        self.conv2 = nn.Conv2d(16,32,3,padding=1)
        
        self.conv3 = nn.Conv2d(32,64,3,padding=1) #5,padding=2)
        
        self.conv4 = nn.Conv2d(64,128,3,padding=1)
        self.conv5 = nn.Conv2d(128,256,3,padding=1)      

        #batch norm layers for normalizing inputs
        self.bn16 = nn.BatchNorm2d(16)
        self.bn32 = nn.BatchNorm2d(32)
        self.bn64 = nn.BatchNorm2d(64)
        self.bn128 = nn.BatchNorm2d(128)
        self.bn256 = nn.BatchNorm2d(256)

        # max pooling layer
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2,padding=0)        
        
        #dropout 
        self.drop = nn.Dropout(0.3)
        
        self.fc1=torch.nn.Linear(256*7*7,1024) #(256*14*14,640)#(256*14*14,640)#(256*28*28,640)
        self.fc2=torch.nn.Linear(1024,133)
        
    
    def forward(self, x):
        ## Define forward behavior
        
        #in: 3 224x224
        x = F.relu(self.conv1(x))
        #out: 16 224x224
        
        x= self.pool(x)
        #out: 16 112x112        
        x = self.bn16(x)
        
        
        x = F.relu(self.conv2(x))
        #out: 32 112x112
                
        x = self.pool(x)
        #out: 32 56x56
        x = self.bn32(x)
        
        x = F.relu(self.conv3(x))
        #out: 64 56x56
        
        x= self.pool(x)
        #out: 64 28x28
        x= self.bn64(x)
        
        x = F.relu(self.conv4(x))
        #out: 128 28x28
        
        x = self.pool(x)
        #out: 128 14x14       
        x = self.bn128(x)
        
        x = F.relu(self.conv5(x))
        x = self.pool(x)
        x = self.bn256(x)
                
        x = x.view(-1,256 * 7 * 7)
        
        x = self.drop(x)        
        x = F.relu(self.fc1(x))
        
        x = self.drop(x)
        x = self.fc2(x)
    
        return x

#-#-# You so NOT have to modify the code below this line. #-#-#

# instantiate the CNN
model_scratch = Net()    

# move tensors to GPU if CUDA is available
if torch.cuda.is_available():
    model_scratch.cuda()
    use_cuda=True

Question 4: Outline the steps you took to get to your final CNN architecture and your reasoning at each step.

Answer:

Inputs to the network are 224x224 images in 3 dimensions (ie RGB color) Layer 1 applies a 3x3 convolution to output 16 convolutions. The relu activation function is applied and a 2x2 pooling layer takes the image size down to 112x112. A batch norm layer is applied to the output.

Layer 2 takes the 16 convolution output of layer 1 and applies a 3x3 convolution to output 32 convolutions. The relu activation function is applied and a 2x2 pooling layer takes the size of each convolution down to 56x56. A batch norm layer is applied to the output.

Layer 3 takes the 32 convolution output of layer 2 and applies a 3x3 convolution to output 64 convolutions. The relu activation function is applied and a 2x2 pooling layer takes the size of each convolution down to 28x28. A batch norm layer is applied to the output.

Layer 4 takes the 64 convolution output of layer 3 and applies a 3x3 convolution to output 128 convolutions. The relu activation function is applied and a 2x2 pooling layer takes the size of each convolution down to 14x14. A batch norm layer is applied to the output.

Layer 5 takes the 128 convolution output of layer 4 and applies a 3x3 convolution to output 256 convolutions. The relu activation function is applied and a 2x2 pooling layer takes the size of each convolution down to 7x7. A batch norm layer is applied to the output.

We now have a total output of 256 convolutions of 7x7 in size. We convert this to a vector to feed into a fully connected classifier.

The fully connected layer takes this vector and creates a hidden layer of 1024 nodes, applying dropout to minimize overfitting, and a relu activation function.

The 2nd fully connected layer takes these 1024 inputs, and outputs 133 for classification

(IMPLEMENTATION) Specify Loss Function and Optimizer

Use the next code cell to specify a loss function and optimizer. Save the chosen loss function as criterion_scratch, and the optimizer as optimizer_scratch below.

In [15]:
import torch.optim as optim

### TODO: select loss function
criterion_scratch = nn.CrossEntropyLoss()#nn.NLLLoss()

### TODO: select optimizer
optimizer_scratch = optim.SGD(model_scratch.parameters(), lr=0.005)#lr=0.001) #optim.Adam()  

(IMPLEMENTATION) Train and Validate the Model

Train and validate your model in the code cell below. Save the final model parameters at filepath 'model_scratch.pt'.

In [16]:
import numpy as np

def train(n_epochs, loaders, model, optimizer, criterion, use_cuda, save_path):
    """returns trained model"""
    # initialize tracker for minimum validation loss
    valid_loss_min = np.Inf 
    
    for epoch in range(1, n_epochs+1):
        # initialize variables to monitor training and validation loss
        train_loss = 0.0
        valid_loss = 0.0
        
        ###################
        # train the model #
        ###################
        model.train()
        for batch_idx, (data, target) in enumerate(loaders['train']):
            # move to GPU
            if use_cuda:
                data, target = data.cuda(), target.cuda()
                if batch_idx == 0 and epoch == 1 : print('using gpu ' + torch.cuda.get_device_name(0))
                
            ## find the loss and update the model parameters accordingly
            ## record the average training loss, using something like
            ## train_loss = train_loss + ((1 / (batch_idx + 1)) * (loss.data - train_loss))
            
            optimizer.zero_grad()
            # forward pass: compute predicted outputs by passing inputs to the model
            output = model(data)
            # calculate the batch loss
            loss = criterion(output, target)
            # backward pass: compute gradient of the loss with respect to model parameters
            loss.backward()
            # perform a single optimization step (parameter update)
            optimizer.step()
            # update training loss
            #train_loss += loss.item()*data.size(0)
            train_loss = train_loss + (1 / (batch_idx + 1)) * (loss.data - train_loss)
            
            
        ######################    
        # validate the model #
        ######################
        model.eval()
        for batch_idx, (data, target) in enumerate(loaders['valid']):
            # move to GPU
            if use_cuda:
                data, target = data.cuda(), target.cuda()
            ## update the average validation loss
            
            # forward pass: compute predicted outputs by passing inputs to the model
            output = model(data)
            # calculate the batch loss
            loss = criterion(output, target)
            # update average validation loss 
            #valid_loss += loss.item()*data.size(0)            
            valid_loss = valid_loss + ((1 / (batch_idx + 1)) * (loss.data - valid_loss))
            
        # print training/validation statistics 
        print('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f}'.format(
            epoch, 
            train_loss,
            valid_loss
            ))
        
        ## save the model if validation loss has decreased
        
        if valid_loss <= valid_loss_min:
            print('Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...'.format(
            valid_loss_min,
            valid_loss))
            torch.save(model.state_dict(), save_path) #'model_scratch.pt')
            valid_loss_min = valid_loss

        
    # return trained model
    return model
In [53]:
num_epochs = 50

# train the model
#this is commented so whole notebook can be ran without having to train.  uncomment this line to train
#model_scratch = train(num_epochs, loaders_scratch, model_scratch, optimizer_scratch, 
                      criterion_scratch, use_cuda, 'model_scratch.pt')

# load the model that got the best validation accuracy
model_scratch.load_state_dict(torch.load('model_scratch.pt'))
using gpu TITAN Xp COLLECTORS EDITION
Epoch: 1 	Training Loss: 4.299505 	Validation Loss: 4.459019
Validation loss decreased (inf --> 4.459019).  Saving model ...
Epoch: 2 	Training Loss: 4.289641 	Validation Loss: 4.299578
Validation loss decreased (4.459019 --> 4.299578).  Saving model ...
Epoch: 3 	Training Loss: 4.266031 	Validation Loss: 4.301987
Epoch: 4 	Training Loss: 4.248266 	Validation Loss: 4.361635
Epoch: 5 	Training Loss: 4.222089 	Validation Loss: 4.194287
Validation loss decreased (4.299578 --> 4.194287).  Saving model ...
Epoch: 6 	Training Loss: 4.209045 	Validation Loss: 4.227835
Epoch: 7 	Training Loss: 4.189783 	Validation Loss: 4.218337
Epoch: 8 	Training Loss: 4.159361 	Validation Loss: 4.238174
Epoch: 9 	Training Loss: 4.160546 	Validation Loss: 4.276725
Epoch: 10 	Training Loss: 4.170162 	Validation Loss: 4.212649
Epoch: 11 	Training Loss: 4.128987 	Validation Loss: 4.193985
Validation loss decreased (4.194287 --> 4.193985).  Saving model ...
Epoch: 12 	Training Loss: 4.120012 	Validation Loss: 4.245790
Epoch: 13 	Training Loss: 4.123603 	Validation Loss: 4.068078
Validation loss decreased (4.193985 --> 4.068078).  Saving model ...
Epoch: 14 	Training Loss: 4.085110 	Validation Loss: 4.159950
Epoch: 15 	Training Loss: 4.083466 	Validation Loss: 4.053330
Validation loss decreased (4.068078 --> 4.053330).  Saving model ...
Epoch: 16 	Training Loss: 4.073523 	Validation Loss: 4.070778
Epoch: 17 	Training Loss: 4.033102 	Validation Loss: 4.449601
Epoch: 18 	Training Loss: 4.060807 	Validation Loss: 4.698535
Epoch: 19 	Training Loss: 4.032651 	Validation Loss: 4.046303
Validation loss decreased (4.053330 --> 4.046303).  Saving model ...
Epoch: 20 	Training Loss: 4.008412 	Validation Loss: 4.090196
Epoch: 21 	Training Loss: 3.976452 	Validation Loss: 4.103407
Epoch: 22 	Training Loss: 3.997099 	Validation Loss: 4.069636
Epoch: 23 	Training Loss: 3.969114 	Validation Loss: 4.022613
Validation loss decreased (4.046303 --> 4.022613).  Saving model ...
Epoch: 24 	Training Loss: 3.956002 	Validation Loss: 4.270702
Epoch: 25 	Training Loss: 3.950153 	Validation Loss: 4.094254
Epoch: 26 	Training Loss: 3.924225 	Validation Loss: 3.959381
Validation loss decreased (4.022613 --> 3.959381).  Saving model ...
Epoch: 27 	Training Loss: 3.925189 	Validation Loss: 3.965192
Epoch: 28 	Training Loss: 3.902164 	Validation Loss: 4.266589
Epoch: 29 	Training Loss: 3.894764 	Validation Loss: 4.032702
Epoch: 30 	Training Loss: 3.887157 	Validation Loss: 3.959627
Epoch: 31 	Training Loss: 3.884608 	Validation Loss: 4.072246
Epoch: 32 	Training Loss: 3.863528 	Validation Loss: 4.015224
Epoch: 33 	Training Loss: 3.868892 	Validation Loss: 3.919291
Validation loss decreased (3.959381 --> 3.919291).  Saving model ...
Epoch: 34 	Training Loss: 3.853758 	Validation Loss: 4.017617
Epoch: 35 	Training Loss: 3.843172 	Validation Loss: 3.951783
Epoch: 36 	Training Loss: 3.814491 	Validation Loss: 3.925235
Epoch: 37 	Training Loss: 3.814198 	Validation Loss: 4.328729
Epoch: 38 	Training Loss: 3.800559 	Validation Loss: 3.897666
Validation loss decreased (3.919291 --> 3.897666).  Saving model ...
Epoch: 39 	Training Loss: 3.780868 	Validation Loss: 4.113053
Epoch: 40 	Training Loss: 3.785684 	Validation Loss: 4.004509
Epoch: 41 	Training Loss: 3.772429 	Validation Loss: 3.928270
Epoch: 42 	Training Loss: 3.780471 	Validation Loss: 4.206999
Epoch: 43 	Training Loss: 3.760798 	Validation Loss: 4.412291
Epoch: 44 	Training Loss: 3.748797 	Validation Loss: 3.841372
Validation loss decreased (3.897666 --> 3.841372).  Saving model ...
Epoch: 45 	Training Loss: 3.746809 	Validation Loss: 4.202786
Epoch: 46 	Training Loss: 3.719809 	Validation Loss: 4.254813
Epoch: 47 	Training Loss: 3.739764 	Validation Loss: 4.031788
Epoch: 48 	Training Loss: 3.726869 	Validation Loss: 3.825253
Validation loss decreased (3.841372 --> 3.825253).  Saving model ...
Epoch: 49 	Training Loss: 3.703450 	Validation Loss: 3.824295
Validation loss decreased (3.825253 --> 3.824295).  Saving model ...
Epoch: 50 	Training Loss: 3.699284 	Validation Loss: 3.818922
Validation loss decreased (3.824295 --> 3.818922).  Saving model ...
Out[53]:
<All keys matched successfully>

(IMPLEMENTATION) Test the Model

Try out your model on the test dataset of dog images. Use the code cell below to calculate and print the test loss and accuracy. Ensure that your test accuracy is greater than 10%.

In [54]:
def test(loaders, model, criterion, use_cuda):

    # monitor test loss and accuracy
    test_loss = 0.
    correct = 0.
    total = 0.

    model.eval()
    for batch_idx, (data, target) in enumerate(loaders['test']):
        # move to GPU
        if use_cuda:
            data, target = data.cuda(), target.cuda()
        # forward pass: compute predicted outputs by passing inputs to the model
        output = model(data)
        # calculate the loss
        loss = criterion(output, target)
        # update average test loss 
        test_loss = test_loss + ((1 / (batch_idx + 1)) * (loss.data - test_loss))
        # convert output probabilities to predicted class
        pred = output.data.max(1, keepdim=True)[1]
        # compare predictions to true label
        correct += np.sum(np.squeeze(pred.eq(target.data.view_as(pred))).cpu().numpy())
        total += data.size(0)
            
    print('Test Loss: {:.6f}\n'.format(test_loss))

    print('\nTest Accuracy: %2d%% (%2d/%2d)' % (
        100. * correct / total, correct, total))
In [55]:
# call test function    
test(loaders_scratch, model_scratch, criterion_scratch, use_cuda)
Test Loss: 3.809599


Test Accuracy: 13% (110/836)

Step 4: Create a CNN to Classify Dog Breeds (using Transfer Learning)

You will now use transfer learning to create a CNN that can identify dog breed from images. Your CNN must attain at least 60% accuracy on the test set.

(IMPLEMENTATION) Specify Data Loaders for the Dog Dataset

Use the code cell below to write three separate data loaders for the training, validation, and test datasets of dog images (located at dogImages/train, dogImages/valid, and dogImages/test, respectively).

If you like, you are welcome to use the same data loaders from the previous step, when you created a CNN from scratch.

In [56]:
## TODO: Specify data loaders

use_cuda = torch.cuda.is_available()
loaders_transfer = loaders_scratch

(IMPLEMENTATION) Model Architecture

Use transfer learning to create a CNN to classify dog breed. Use the code cell below, and save your initialized model as the variable model_transfer.

In [57]:
import torchvision.models as models
import torch.nn as nn
import numpy as np



## TODO: Specify model architecture 
model_transfer = models.resnet50(pretrained=True)

#freeze the params in the model so that they are not changed
for param in model_transfer.parameters():
    param.requires_grad = False   

#resnet
model_transfer.fc = nn.Linear(2048,133)
    
if use_cuda:
    model_transfer.cuda()
    print("Using CUDA")  
    
print(model_transfer)
Using CUDA
ResNet(
  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (relu): ReLU(inplace=True)
  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
  (layer1): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
  )
  (layer2): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (3): Bottleneck(
      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
  )
  (layer3): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (3): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (4): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (5): Bottleneck(
      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
  )
  (layer4): Sequential(
    (0): Bottleneck(
      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (downsample): Sequential(
        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): Bottleneck(
      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
    (2): Bottleneck(
      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
    )
  )
  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
  (fc): Linear(in_features=2048, out_features=133, bias=True)
)

Question 5: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. Describe why you think the architecture is suitable for the current problem.

Answer:

I used the ResNet architecture because I have read that it performs very well for image classification. Resnet50 being a good intermediate depth. I simply replaced the final "fc" layer of resnet, with a fc layer that takes the 2048 inputs from the previous layer, and connects them to 133 layers for classification

(IMPLEMENTATION) Specify Loss Function and Optimizer

Use the next code cell to specify a loss function and optimizer. Save the chosen loss function as criterion_transfer, and the optimizer as optimizer_transfer below.

In [58]:
import torch.optim as optim

criterion_transfer = nn.CrossEntropyLoss() #nn.NLLLoss()
optimizer_transfer = optim.Adam(model_transfer.fc.parameters(),lr=.003)

(IMPLEMENTATION) Train and Validate the Model

Train and validate your model in the code cell below. Save the final model parameters at filepath 'model_transfer.pt'.

In [59]:
n_epochs = 10

# train the model #uncomment to train
#model_transfer = train(n_epochs, loaders_transfer, model_transfer, optimizer_transfer, criterion_transfer, torch.cuda.is_available(), 'model_transfer.pt')

# load the model that got the best validation accuracy (uncomment the line below)
model_transfer.load_state_dict(torch.load('model_transfer.pt'))
Out[59]:
<All keys matched successfully>

(IMPLEMENTATION) Test the Model

Try out your model on the test dataset of dog images. Use the code cell below to calculate and print the test loss and accuracy. Ensure that your test accuracy is greater than 60%.

In [60]:
test(loaders_transfer, model_transfer, criterion_transfer, use_cuda)
Test Loss: 0.490177


Test Accuracy: 84% (709/836)

(IMPLEMENTATION) Predict Dog Breed with the Model

Write a function that takes an image path as input and returns the dog breed (Affenpinscher, Afghan hound, etc) that is predicted by your model.

In [61]:
### TODO: Write a function that takes a path to an image as input
### and returns the dog breed that is predicted by the model.
from PIL import Image
use_cuda = torch.cuda.is_available()

# list of class names by index, i.e. a name can be accessed like class_names[0]
#class_names = [item[4:].replace("_", " ") for item in data_transfer['train'].classes]
class_names = [item[4:].replace("_", " ") for item in loaders_transfer['train'].dataset.classes]


def predict_breed_transfer(img_path):
    # load the image and return the predicted breed
    #print('img path: ' + img_path)
    #load image into pillow object
    image = Image.open(img_path)
    
    #model already loaded above
    #put model in evaluation mode
    model_transfer.eval()
    
    #TODO:  apply transforms to input image
    image_tensor =  test_trans(image).float()    
    image_tensor = image_tensor.unsqueeze_(0)
    
    if use_cuda: image_tensor = image_tensor.cuda()
        
    output = model_transfer(image_tensor)
    index = output.data.cpu().numpy().argmax()
    return index

Step 5: Write your Algorithm

Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither. Then,

  • if a dog is detected in the image, return the predicted breed.
  • if a human is detected in the image, return the resembling dog breed.
  • if neither is detected in the image, provide output that indicates an error.

You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the face_detector and human_detector functions developed above. You are required to use your CNN from Step 4 to predict dog breed.

Some sample output for our algorithm is provided below, but feel free to design your own user experience!

Sample Human Output

(IMPLEMENTATION) Write your Algorithm

In [62]:
def display_img(img_path):
    img = cv2.imread(img_path)
    cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    plt.imshow(cv_rgb)
    plt.show()    
In [67]:
### TODO: Write your algorithm.
### Feel free to use as many code cells as needed.


def run_app(img_path):
    ## handle cases for a human face, dog, and neither
    print("################################")
    print("Predicting: " + img_path)
    display_img(img_path)
    if dog_detector(img_path): 
        print("DOG")
        print("  Hello doggo! Whos a good boy?! Looks like you are a: ")
        
        id = predict_breed_transfer(img_path)
        #print(id)
        print("  -- "+ class_names[id])
    elif face_detector(img_path):
        print("HUMAN")
        print("  Hello Human,  if you were a dog,  you would be a...")
        
        id = predict_breed_transfer(img_path)    
        print(" -- "+class_names[id])
        
    else: 
        print("UNKNOWN")
        print("  bzzzt, error.  Are you an alien?")
        
        
    print("")
    print("")

Step 6: Test Your Algorithm

In this section, you will take your new algorithm for a spin! What kind of dog does the algorithm think that you look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog?

(IMPLEMENTATION) Test Your Algorithm on Sample Images!

Test your algorithm at least six images on your computer. Feel free to use any images you like. Use at least two human and two dog images.

Question 6: Is the output better than you expected :) ? Or worse :( ? Provide at least three possible points of improvement for your algorithm.

Answer:

  • It does not do well with dogs lying down. I would include more training data of sitting / lying dogs in the training set
  • Tweaking hyperparamaters like learning rate, maybe more fully connected layers in the transfer learning section could raise the accuracy above 84% and imrove classification errors such as the husky that was classified as a norwegian lundehund
  • I had fun with this and wanted to see what it would do with other animals not just dogs and humans. The human detector is quite bad! A goat, a bear and a camel were all classified as humans. I would improve human detection, or maybe even including humans as a class in the dog training set and see how accurate that is
In [64]:
## TODO: Execute your algorithm from Step 6 on
## at least 6 images on your computer.
## Feel free to use as many code cells as needed.
import glob
   
dogpath = "data/my_images/dogs/*"
for img_d in glob.iglob(dogpath):    
    run_app(img_d)

   
################################
Predicting: data/my_images/dogs/penguin.jpeg
 bzzzt, error.  Are you an alien?


################################
Predicting: data/my_images/dogs/arctic wolf 1.jpeg
 bzzzt, error.  Are you an alien?


################################
Predicting: data/my_images/dogs/giraffe.jpeg
  Hello Human,  if you were a dog,  you would be a...
 -- Dalmatian


################################
Predicting: data/my_images/dogs/hounds.jpeg
  Hello doggo! Whos a good boy? Looks like you are a: 
  -- Basset hound


################################
Predicting: data/my_images/dogs/goat.jpeg
  Hello Human,  if you were a dog,  you would be a...
 -- Norwegian lundehund


################################
Predicting: data/my_images/dogs/aussie 3.jpeg
  Hello doggo! Whos a good boy? Looks like you are a: 
  -- Australian shepherd


################################
Predicting: data/my_images/dogs/tiger.jpeg
 bzzzt, error.  Are you an alien?


################################
Predicting: data/my_images/dogs/white tiger.jpeg
 bzzzt, error.  Are you an alien?


################################
Predicting: data/my_images/dogs/timberwolf1.jpeg
  Hello Human,  if you were a dog,  you would be a...
 -- Norwegian elkhound


################################
Predicting: data/my_images/dogs/mutt.jpeg
  Hello doggo! Whos a good boy? Looks like you are a: 
  -- Norwegian lundehund


################################
Predicting: data/my_images/dogs/samoyed.jpeg
  Hello doggo! Whos a good boy? Looks like you are a: 
  -- American eskimo dog


################################
Predicting: data/my_images/dogs/bulldog pool.jpeg
  Hello doggo! Whos a good boy? Looks like you are a: 
  -- Bulldog


################################
Predicting: data/my_images/dogs/boston terrier fat.jpeg
  Hello doggo! Whos a good boy? Looks like you are a: 
  -- Bull terrier


################################
Predicting: data/my_images/dogs/dogwater.jpeg
  Hello Human,  if you were a dog,  you would be a...
 -- Glen of imaal terrier


################################
Predicting: data/my_images/dogs/grizzly.jpeg
  Hello Human,  if you were a dog,  you would be a...
 -- Newfoundland


################################
Predicting: data/my_images/dogs/lioness1.jpeg
 bzzzt, error.  Are you an alien?


################################
Predicting: data/my_images/dogs/camel.jpeg
  Hello Human,  if you were a dog,  you would be a...
 -- Chesapeake bay retriever


################################
Predicting: data/my_images/dogs/mountain goat.jpeg
 bzzzt, error.  Are you an alien?


################################
Predicting: data/my_images/dogs/lion1.jpeg
 bzzzt, error.  Are you an alien?


################################
Predicting: data/my_images/dogs/sled dog 2.jpeg
  Hello doggo! Whos a good boy? Looks like you are a: 
  -- Norwegian elkhound


################################
Predicting: data/my_images/dogs/arctic wolf 2.jpeg
 bzzzt, error.  Are you an alien?


################################
Predicting: data/my_images/dogs/husky.jpeg
  Hello doggo! Whos a good boy? Looks like you are a: 
  -- Norwegian lundehund


################################
Predicting: data/my_images/dogs/dog car.jpeg
  Hello doggo! Whos a good boy? Looks like you are a: 
  -- Afghan hound


################################
Predicting: data/my_images/dogs/timberwolf2.jpeg
  Hello Human,  if you were a dog,  you would be a...
 -- Norwegian elkhound


################################
Predicting: data/my_images/dogs/lion2.jpeg
 bzzzt, error.  Are you an alien?


################################
Predicting: data/my_images/dogs/aussie_upsidedown.jpeg
 bzzzt, error.  Are you an alien?


################################
Predicting: data/my_images/dogs/aussie 2.jpeg
  Hello doggo! Whos a good boy? Looks like you are a: 
  -- Australian shepherd


################################
Predicting: data/my_images/dogs/bunny.jpeg
 bzzzt, error.  Are you an alien?


################################
Predicting: data/my_images/dogs/tiger2.jpeg
 bzzzt, error.  Are you an alien?


################################
Predicting: data/my_images/dogs/aussie 1.jpeg
  Hello doggo! Whos a good boy? Looks like you are a: 
  -- Collie


################################
Predicting: data/my_images/dogs/cheetah.jpeg
 bzzzt, error.  Are you an alien?


################################
Predicting: data/my_images/dogs/polar bear.jpeg
 bzzzt, error.  Are you an alien?


################################
Predicting: data/my_images/dogs/sleddog1.jpeg
  Hello doggo! Whos a good boy? Looks like you are a: 
  -- Alaskan malamute


################################
Predicting: data/my_images/dogs/eskie1.jpeg
  Hello doggo! Whos a good boy? Looks like you are a: 
  -- American eskimo dog


################################
Predicting: data/my_images/dogs/llama.jpeg
 bzzzt, error.  Are you an alien?


################################
Predicting: data/my_images/dogs/eskie2.jpeg
  Hello doggo! Whos a good boy? Looks like you are a: 
  -- Pomeranian


In [66]:
humanpath = "data/my_images/people/*"

for img_h in glob.iglob(humanpath):
    run_app(img_h)
    
################################
Predicting: data/my_images/people/andrew.jpeg
  Hello Human,  if you were a dog,  you would be a...
 -- Glen of imaal terrier


################################
Predicting: data/my_images/people/me.jpeg
  Hello Human,  if you were a dog,  you would be a...
 -- Welsh springer spaniel


################################
Predicting: data/my_images/people/suzie.jpeg
 bzzzt, error.  Are you an alien?


In [ ]: